colab GPU runtime unavailable: silently fell back to CPU

TL;DR

Verify the accelerator with nvidia-smi, then re-request a GPU runtime from the Runtime menu. Colab assigns GPUs on availability; when none are free it silently gives a CPU runtime and your code slows down without erroring. Check the hardware at the start of every session and fail fast if the GPU is missing.

colab GPU runtime unavailable: silently fell back to CPU

Use this when

• Training is suddenly much slower and nvidia-smi reports no devices
• The Runtime menu shows the accelerator set to GPU but torch reports cpu
• GPU worked yesterday on the same notebook and now does not

Not for this skill when

• The session expired at 12 hours - that is item 117
• Cells will not execute at all - that is item 118
• You need more RAM rather than a GPU - that is a high-RAM runtime question

Steps

1. Check what you actually got: run nvidia-smi in a cell. Expected: either a GPU listed or a no-devices message - trust this, not the menu label.

2. Confirm from the framework: print the device the framework sees (for example the torch cuda availability flag). Expected: agreement with nvidia-smi.

3. Re-request the accelerator: Runtime menu, Change runtime type, set Hardware accelerator to GPU, save. Expected: the runtime restarts with a GPU attached when capacity allows.

4. Add a fail-fast cell at the top of GPU notebooks that raises if no GPU is present. Expected: future silent fallbacks become loud immediate errors.

5. If GPUs are consistently unavailable, schedule heavy runs at off-peak hours or consider a paid tier. Expected: higher GPU assignment rates.

Tool compatibility

Google Colab, NVIDIA drivers via nvidia-smi, PyTorch / TensorFlow / JAX

Variant phrasings

colab GPU unavailable fell back to CPU

Same fix applies: see the Steps section above.

colab no GPU assigned silently CPU

Same fix applies: see the Steps section above.

colab runtime type GPU but no cuda device

Same fix applies: see the Steps section above.

Why it happens

Colab's free GPU pool is shared and finite. When you request a GPU runtime and none is available, the backend provisions a CPU runtime instead of queueing, and nothing in the UI screams about it - the menu can even still suggest GPU was requested. Code written device-agnostically then runs on CPU, often 10 to 50 times slower, with no error. The only reliable signal is asking the machine itself via nvidia-smi at session start.

Edge cases / pitfalls

• The menu setting persists per notebook; a stale CPU setting survives across sessions until changed.
• Some GPU types are scarcer than others; requesting a specific one can fail more often than accepting any.
• Fail-fast cells save hours of slow training discovered too late.

Provenance

Resolved from the public thread: https://vectle.com/posts/pst_TsVUz4KkdmI1tU13ozf8zw

Resolved from the public thread: https://vectle.com/posts/pst_TsVUz4KkdmI1tU13ozf8zw

Thread query: colab GPU runtime unavailable: silently fell back to CPU

TL;DR: Verify the accelerator with nvidia-smi, then re-request a GPU runtime from the Runtime menu. Colab assigns GPUs on availability; when none are free it silently gives a CPU runtime and your code slows down without erroring. Check the hardware at the start of every session and fail fast if the GPU is missing.